sustainable tourism networks - texas a&m...

BEST EN Think Tank X Networking for Sustainable Tourism

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Sustainable Tourism Networks

Seldjan Timur

Abstract

This study examines the existing pattern of stakeholder relationships representing major partners

of sustainable tourism development. By utilizing a network analysis lens the study also helps us

understand the impact of inter relationships of destination stakeholders on sustainable tourism

development. It is found that local DMOs are the stakeholders with a high centrality position in

destination networks. The positional characteristic of DMOs emphasizes that local tourism

organizations have important decisional roles not only for destination marketing but also

sustainable tourism development.

Introduction

The social network approach views organizations in a society as a system of objects where objects

could be people, groups, or organizations, and these objects are joined by a variety of relationships

(Tichy, Tushman and Fombrun, 1979). Objects could be directly or indirectly linked, not joined or

joined by multiple relationships. Network analysis is concerned with structure and patterning of

these relationships and seeks to identify both their causes and consequences (Tichy et al.,

1979:507).

The purpose of network analysis is to examine relational systems in which actors dwell through the

internal location of a set of nodes linked by a set of relationships and to determine how the nature

of relationship structures impacts behaviors (Lauman et al. 1978). Network analysts examine the

pattern of relationships between members of the relevant network, arguing that their positions in

networks influence their opportunities, constraints, and behaviors (Wasserman and Galaskiewicz,

1994).

The three concepts, which are important in understanding network analysis, are “nodes” (also

called actors), “links”, and “networks”. “Nodes” are entities, persons, organizations, or events that

define the network. In the current study actors are the stakeholders, more specifically potential

sustainable tourism development partners.

“Links” are the relationships between the actors and they can be any type of relationships. Links

may be money transfers among businesses, communication among people or organizations,


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exchange of resources, transfer of information, and so on. Links have content (Cobb, 1988). In this

study, joint tourism business contacts are analyzed. The link is having business contacts, and the

content of the link is the joint tourism programs or projects.

“Networks” are the patterns formed from the combination of all the actors and links within the

system. The pattern of network member relationships can be examined by measures such as

density and centrality (Rowley, 1997; Burt, 1980; Galaskiewicz, 1979; Scott, 2000; Krackhardt,

1990). Networks may be “dense” (having many links) or “sparse” (having few links) throughout the

system. Density refers to the number of connections between actors within the network. It is

argued that highly dense networks result in efficient communication and enhanced diffusion of

norms across networks because of many ties between network members (Meyer and Rowan,

1977; Galaskiewicz and Wasserman, 1989).

Another network characteristic is “centrality”. Network centrality refers to an individual actor's

position in the network relative to others. Networks may have one central actor with links from

many actors directed to it, which indicates high network centrality, or a network may have several

groups and no central actor that indicates low network centrality. Centrality tries to capture the

property of actors in terms of links with others (Freeman, 1979). Network centrality refers to power

obtained through the network's structure (Rowley, 1997; Barley et al, 1992). Highly central actors

in the network are those who have important decisional and meditative roles, and who are the key

to understanding the circulation of ideas and decisions to act collectively, particularly when the

individuals are in different organizations (John and Cole 1998). The network literature also

suggests that actors that are more central within a network have more influence (related to

increased legitimacy) than those that are more peripheral. A central position within the network

indicates the amount of power obtained through the structure, and capacity to access information

and other members.

There are various measures of centrality (Freeman, 1979; Scott, 2000; Wasserman and Faust,

1994). Freeman (1979) in his influential study operationalized centrality by “degree”,

“betweenness” and “closeness” measures. “Degree” based centrality is simply the number of other

actors to which the focal actor is tied (Krackhardt, 1990; Freeman, 1979). Degree centrality

measures an actor's involvement in a network by telling how many connections an actor has. It

corresponds to being well connected, that is, how well connected a point is within its local

environment (Scott, 2000). It could be computed for in-degree centrality and out-degree centrality

of the various stakeholders. While centrality measure based on in-degree measures how many ties

an actor receives, out-degree based centrality measures how many ties are made with other

actors. Actors who receive many ties have high in-degree centrality, which may indicate their


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importance since many others seek direct ties to them. Actors who make many ties with others

have high out-degree centrality, and they are able to exchange with many others and maybe make

many others aware of their views (Hanneman 2001). “Betweenness” centrality is the extent to

which actors fall between pairs of other actors on the shortest paths connecting them. It measures

the frequency with which an actor falls on the paths between pairs of other actors (Freeman,

1979). “Closeness” centrality defines an actor's ability to access independently all other members

of the network (Freeman, 1979). When the stakeholder is 'close' to all others (high closeness), it is

less dependent on others. Studies have found that the more other organizations are dependent

upon a focal organization for the resource they need, the more likely those organizations are going

to be viewed as influential.

This study uses a network analysis lens to examine structural characteristics of destination

stakeholders and the impact of stakeholder relations on destination’s sustainable tourism

development efforts. The interest of adapting network perspective in this study lies in the

recognition that destination stakeholder environment consists of multiple and interdependent

relations that are likely to influence stakeholder values and consequently sustainable tourism

processes. Employing network analysis can help more fully understand relationships between

destination stakeholders, analyze the characteristics of entire stakeholder structure, and examine

how the pattern of linkages between stakeholders can attain strategic leverage for destinations.

The WTO (1993) defines major partners for sustainable tourism development as the industry,

environment supporters and community/local authority. In this study, the community stakeholder

group is integrated with the environment supporters and this group is named as local host

environment. The local host environment group includes both local community and environment

supporters (groups dedicated to preserve, conserve and enhance social, cultural, and natural

resources). The community/local authorities group is renamed as government agencies and

represents all pertinent government agencies. The main clusters and potential key stakeholders in

each cluster are presented in Figure 1. This framework not only shows three major partners of

sustainable tourism development but also corresponds to the sample groups used in the study.


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Figure 1 Stakeholder Groups for Sustainable Tourism Development

Network Analysis Research Design

To gather network data information must be obtained on all relations among actors (Burt, 1980).

However, deciding which actors to include in the network (that is, defining network boundaries) has

been among the challenges of network analysis (Rowley, 1997; Cobb, 1988; Nohria and Garcia-

Pont, 1991). While a small-scale network (a closed system of actors) such as a university faculty or

Convention and Visitors’ Bureau, provides a clear view of relevant participants, more complex

systems such as inter-organizational fields or community networks can be problematic for

boundary specification. There is no formal solution to decide which actors to include in the network.

The choice depends on the substantive focus of the study under investigation (Nohria and Garcia-

Pont, 1991; Cobb, 1988; Scott, 2000). However, there are various methods used to specify the

boundaries of networks or, in other words, to define the target population. Three alternative

approaches are offered: positional (attributes of actors), reputational, or a central issue or event

providing the setting for the study (decision/participation method) (Scott, 2000; Knoke, 1994; Pforr,

2002).

In the positional approach, samples are selected from among the occupants of particular formally

defined positions or membership in a formal organization. For example, highest ranking executive

officers of tourism organizations, members of Convention and Visitors’ Bureau and so on. The

participation method is a strategy of selection, which would be concerned with choosing people

who are involved in, for instance, an activity, event, or an issue independently of any positions or

organizations that may have been used to identify the people themselves (Scott, 2000; Knoke,

TOURISM INDUSTRY (Attractions, accommodation, food & beverage, tour operators, retail shops, DMO,

industry associations, etc. )

LOCAL HOST ENVIRONMENT (host community and resource base of the host environment. i.e., residents, community groups, local organizations, institutions, and associations, and environment supporters i.e. groups dedicated to preserve, conserve and enhance social, cultural, and natural resources).

GOVERNMENT AUTHORITIES

(Government agencies at all levels)


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1994). For example, participation in tourism policy making in the Northern Territory of Australia was

used as the basis of actor selection by Pforr (2002).

The reputational approach is usually used where no relevant positions or comprehensive listing

available, or where the knowledge of agents themselves is crucial in determining the boundaries of

the population (Scott, 2000; Knoke, 1994). In the reputational approach, the researcher studies all

or some of those named on a list of nominees produced by knowledgeable informants.

In the present study, the participation and reputational approaches were combined to specify

network boundaries. The participation method identified a potential list of eligible organizations in

tourism destinations from existing sources. To determine the core actors involved in STD, public

documents such as the World Tourism Organization publications, economic development plans for

cities, annual reports of destination management organizations, and newspapers were reviewed.

The reputational approach was also employed. At the initial phase of the study, background

interviews in three sample cities were completed. During this initial study, lists of stakeholders were

developed through a "snowball technique." The initial interviewee list was composed of core

tourism actors. Information from these knowledgeable respondents was collected, and they were

also asked to nominate other stakeholders that either they were in regular contact with or any

others that they perceived to be important and powerful stakeholders for tourism planning,

development and policy formulation purposes. This process continued until no new stakeholder

names were suggested by interviewees (i.e., where a saturation point is reached). The final

stakeholder lists composed for each city included not only core tourism stakeholders but also their

nominees.

Data Collection

Once a network has been identified, network analysis requires collecting data from all members.

But, in some cases it might be necessary to use sample data (Ibarra, 1993; Scott, 2000). For

instance, when research on a large scale is undertaken a particular population of agents could be

involved in a complex system of relations of all types that make up the total network (Scott, 2000).

Three separate tourism networks were chosen for empirical investigation in this study. Data were

gathered from Calgary (Alberta, Canada), Victoria (BC, Canada) and San Francisco (California,

USA). The members of the tourism destination networks in three cities were identified. Three lists,

one for each city, were compiled. Each list included stakeholders from three clusters (Figure 1). It

was clear that it would be almost impossible to achieve a complete data and examine all relations

between destination stakeholders. Presumably, there were a finite number of government


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agencies, and a potentially infinite number of organizations and representatives in the tourism

industry and host environment clusters. Examining only the relationships between the tourism

industry members could be quite complicated. Moreover, analyzing one sub-sector of the tourism

industry such as accommodation or food and beverages could be considered quite complicated.

The aim of using a sampling technique in this study was to achieve a representative sample of the

target population from which all existing relationships between the members of the three clusters

could be examined.

A total of 578 questionnaires were mailed to government agents respectively in Calgary, Victoria

and San Francisco. The questionnaires were sent to the stakeholders representing organizations

and entities from tourism industry, government authorities and host environment.

The works of Galaskiewicz (1979), Tichy et al (1979), Cobb (1988), Krackhardt (1990), Ibarra

(1993), and John and Cole (1998) provided the structure of network analysis questions. To

measure existing inter-stakeholder relationships, the tourism contact networks based on joint

tourism projects or programs were selected. This would help describe the interconnectedness of

destination stakeholders.

To examine existing stakeholder relations across tourism contact network, a list of stakeholders

was developed. It was necessary to give a standard list because in a destination context, there are

numerous and various actors, groups, and organizations not only from different sub-sectors of

tourism industry (as product and service suppliers) but also from the public sector (such as

regulators, planners) and other local organizations, associations, and institutions. To help

respondents identify major stakeholders in three clusters (refer to Figure 1) a list was developed.

The classifications used by the World Tourism Organization and major tourism textbooks were

used to prepare a standard list. The alternative was to have a different list named by every

respondent. However, this list could have been unlimited and more time consuming to analyze.

Additionally, for the purposes of the study, identification of exact names of individual stakeholders

or stakeholder groups in contact was less important than their relevant categories, such as

attractions, transportation, or accommodation. Therefore, a standard list was given to respondents

not only to control but also categorize their responses. The standard list was presented to

respondents and they were asked to check off those stakeholders with which their organization

had joint programs or projects (such as joint product development, joint marketing, joint training,

etc.) in the last 12 months.


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Findings

One-hundred-eighty-eight surveys were returned. 70 out of 190, 65 out of 195, and 53 out of 193

surveys were returned respectively from Calgary, Victoria, and San Francisco. Fifteen of returned

responses could not be used due to missing data and incomplete responses. After incomplete

cases had been eliminated, the final sample consisted of 70 questionnaires from Calgary, 62

questionnaires from Victoria, and 41 questionnaires from San Francisco. The final sample size

was 173. This indicates a 29.9% response rate.

Their responses were used to construct an adjacency matrix by coding the presence (or absence)

of a formal business contact a matrix where the stakeholders are both rows and columns was

created. A ‘1’ stands for the presence of a formal business contact between stakeholder i and

stakeholder j, and a ‘0’ indicates the lack of relationship. A matrix for each urban destination was

created. Constructed adjacency matrices were entered into UCINET VI, a software package that

allows the computation of various network measures (Borgatti, Everett, and Freeman, 2002).

UCINET VI also allows the ‘collapsing’ of individual stakeholder responses into stakeholder groups.

In each city, responses from various hotels were collapsed into a ‘hotels’ stakeholder group;

responses from various cultural attractions were collapsed into a ‘cultural attractions’ stakeholder

groups; responses from educational, financial, and/or religious institutions were collapsed into an

‘institutions’ stakeholder group and so on.

Network Mapping

Network analysis provides a visual map to illustrate the structural connectivity. In other words, how

actors are related to one another based on the specific criterion under investigation is displayed.

Visual diagrams of these relational patterns are displayed for each city (Figures 2, 3, and 4). The

maps provided a summary of ties in each destination’s tourism contact networks. The maps

showed links among all destination stakeholders in three clusters from a whole network

perspective. The diagrams also display ego-centric links for each stakeholder. What maps did not

explicitly show were the relationships between clusters. However, in order to overcome this

difficulty and help reader identify stakeholders in each cluster, a different symbol (e.g., ‘○’ for

tourism industry, ‘∆’ for host environment, and ‘□’ for government) was used to represent

stakeholders in these clusters.


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Figure 2 Calgary Network

KEY for Stakeholders : Tourism Industry : Government : Host environment


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Figure 3: Victoria Network



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Figure 4: San Francisco Network

The relational pattern of Calgary network (Figure 2) displayed that Tourism Calgary (the local

DMO) and cultural attractions were at the centre of the network; travel trade, recreational

operators, car rentals, Calgary airport authority, tourism services, Calgary downtown association,

and hotels surrounded them; and local institutions, Calgary convention centre, NGOs, community

groups, B&Bs, natural attractions, motels, rail industry, and Calgary economic development were

peripherally located. The Victoria network (Figure 3) illustrated that Tourism Victoria (the local

DMO), cultural attractions, hotels, motels, recreational and institutions were at the centre of the

network and Victoria conference centre, NGOs, tourism services, travel trade, and air carrier

stakeholder groups surrounded them. Also there were no relational ties for example, between

cruise companies and car rentals, or between B&Bs and national tourism organization. In San

Francisco’s network (Figure 4), San Francisco Convention and Visitors Bureau (SFCVB – the local

DMO), cultural and natural attractions, the City of San Francisco, hotels and travel trade were at

the centre of the network; Moscone Convention Centre, NGOs, recreational operators,

entertainment organizations, and the Chamber of Commerce surrounded them; transportation

subsector, B&Bs, airport authority and institutions were at the periphery, and the cruise stakeholder

group was an isolate in the San Francisco network.



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The government authorities in Calgary had connections with most of the host environment

stakeholders and some of the tourism industry stakeholders. A similar result was revealed in

Victoria. Though the government authorities have very little joint projects or programs with tourism

industry stakeholders, they had relationships with most of the host environment stakeholders.

When between cluster analysis was applied to the San Francisco network, it was found that San

Francisco government authorities had established joint tourism programs or projects with almost

every stakeholder in the host environment cluster and some relationships with tourism industry

stakeholders. Regarding the relationships between tourism industry and host environment clusters,

it was found that, in Calgary, most of the host environment stakeholders had ties with Tourism

Calgary, and tourism industry stakeholders had ties with Calgary Airport Authority, Calgary

Chamber of Commerce, and community groups or clubs. In Victoria, ties between tourism industry

and host environment cluster existed mainly through two channels; host environment stakeholders

established ties usually with Tourism Victoria, and tourism industry stakeholders generally

partnered with community groups for their joint tourism programs and projects. In San Francisco,

two clusters had ties with each other and it was noticed that tourism industry stakeholders

generally had joint tourism programs or projects with the San Francisco Chamber of Commerce.

The maps display relational patterns or structural connectivity in each city. From a whole network

perspective, the network maps identified different patterns of relationships between destination

stakeholders in each city. These patterns – affected by sampling – are likely to reflect how tourism

functions in each destination.

To more fully understand the networks, individual network member (i.e., ego-network) centrality

measures are computed. Centrality is concerned with the positions of destination stakeholders

within the contact network. Centrality can be measured by various measures. Of researchers who

tried to decide which centrality measure was most meaningful and valid for their research

purposes, some explored the conceptual foundations of centrality measures (Freeman 1979) and

others studied the empirical performance of centrality measures under different research scenarios

(Galaskiewicz, 1991; Costenbader and Valente, 2003). Costenbader and Valente (2003), who

studied how sampling affected the stability of various network centrality measures, found that some

centrality measures were more stable than the others, indicating that these measures would be

more appropriate to compute when respondents were not interviewed or did not respond. They

reported that, "in-degree centrality is relatively stable even at a low sampling level ... and as an

indicator of network position" (Costenbader and Valentele, 2003: 306). They argued that the in-

degree centrality measure was less affected by sampling because although respondents dropped

from the sample were no longer able to indicate their ties, they were still able to receive them.


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Therefore, to compute the most meaningful centrality measure for the current study, in-degree

based centrality, which is less affected by sampling, was used.

Based on the in-degree centrality measure, Tourism Calgary and Travel Alberta, Tourism Victoria

and cultural attractions, and San Francisco convention and visitors bureau (SFCVB) were the

stakeholders with a high centrality position in Calgary, Victoria and San Francisco networks,

respectively. Tourism Calgary and Travel Alberta had the highest number of links in Calgary

network. They were partners with 73% of stakeholder groups. In other words, they both had joint

projects or programs with 73% of stakeholders. The hotels stakeholder group was the next most

popular stakeholder group in Calgary. While these were the stakeholders with more ties to others,

B&B and motel stakeholder groups received very few connections from others. Based on

stakeholders’ connections to other stakeholders, B&Bs and motel stakeholder groups could be

interpreted as peripherals in network terms. So, not all industry members were equal.

In Victoria, centrality measures indicate that Tourism Victoria and cultural attractions are the two

stakeholder groups with the highest centrality measure. Sixty-seven percent of respondents chose

Tourism Victoria and cultural attractions (that includes industry stakeholders such as museums,

galleries, historical sites, cultural events and festivals, concerts and theatres) as their business

partners. Cultural attractions are significant tourism business partners because Victoria and

Calgary do not have major private attractions. Tourism BC was the next visible central stakeholder

in Victoria. The analysis also indicated that Capital Regional District (CRD) and Business Victoria

were peripheral stakeholder groups with few joint tourism projects with other stakeholders within

the Victoria contact network.

In San Francisco, the favored position belonged to SFCVB. The results indicated that SFCVB had

connections with 50% of stakeholders. The attractions sector and the City of San Francisco were

also among the destination stakeholders that had high centrality in San Francisco’s network. More

specifically, museums, galleries, architecture and historical sites of the “cultural attractions”

stakeholder group; coastlines, parks and gardens from the “natural attractions” stakeholder group;

and stakeholders such as shopping facilities, performing arts centers, sport complexes and casinos

from the “entertainment group” were significant tourism business partners in San Francisco. Unlike

the other two cities, the City of San Francisco had many ties to other stakeholders in the San

Francisco contact network, indicating its importance as a significant tourism partner. Results also

indicated that the cruise industry was an isolate in this network. In other words, none of the

respondents had joint projects with the cruise industry.


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In summary, it was found that in each city DMOs had many ties with other members of the

destination, indicating that DMOs had better access to all others in the destination. Given that most

of the respondents came from the tourism industry, this in part is a function of the sampling.

Conclusion

The existing structural positions of stakeholders representing three diverse clusters of STD in three

cities displayed that the stakeholders located at the center of networks in the three cities were the

DMOs. But, other stakeholders with high centrality were different in each city. However, they were

stakeholders with access to or possession of critical resources. It is argued that since each

destination faces a different set of key stakeholders, the interactions would probably aggregate into

unique patterns of influences in each city. As a result, sustainable destination development will be

as unique as their historical patterns of development, the nature of their industry, and

governmental and institutional culture.

The entity most likely to take active role in sustainable destination development is the local DMO.

The DMOs could be key players in not only marketing and/or management but also planning and

development, and linking planners, investors, developers, residents, local organizations, and the

industry for developing a sustainable policy for their destinations.

The power of DMOs arises not only from holding a high central position within the destination

network, but also from the dependency of stakeholders on DMOs for resources such as expertise,

information, and clientele. DMOs have the most crucial roles in achieving inter-stakeholder

collaboration for developing a shared tourism policy, particularly because the many and diverse

industry actors trust or depend on them. The other critical stakeholders in destination development

were hotels, attractions, and government agents. These critical stakeholders that had

advantageous positions in the structure of destination networks also have important decision-

making roles, and are key to understanding the circulation of ideas and decisions to act

collectively, particularly when the individuals are in different organizations. From this perspective,

the DMOs, hotels and attractions stakeholders can be used to communicate destination planning

and development issues, facilitate collaboration among stakeholders, increase awareness of

network members towards sustainability challenges, and coordinate efforts toward reaching shared

tourism and hospitality industry goals. In each city, all of these influential stakeholders came from

the industry cluster. This could be related to the sampling but at same time highlights the lack of

“bridges” between the clusters. The DMOs, hotels and attractions stakeholders have another major

role to play in between-cluster networking. They must partner with the bridging stakeholders so


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that contacts between clusters can be established. Establishing ties with less connected or isolated

stakeholders would help minimize the evident disconnect between clusters and improve legitimacy

for sustainable tourism development. Destinations can no longer ignore various stakeholder

concerns. On the contrary, they are challenged to create a more participative model. According to

network theory, to create an environment in which collective action can be realized, more contacts

have to be established. Thus, there is a need for sustainability networks. The term sustainability

networks is used to indicate the interactions of multiple stakeholders with varying degrees of

interest in sustainable destination development. The interconnectedness of diverse stakeholders

representing governmental bodies, business firms, persons or other entities on sustainability

dimensions can improve the process of sustainable destination development.

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